Field of the Invention
The present invention is directed to a method of computational chemistry for identifying binding sites by molecular dynamics simulations using ligand competitive saturation. In particular, the method overcomes the problem of small nonpolar molecule aggregation to allow competitive saturation in an aqueous solution at physiological conditions. More particularly, the method, when used in a two-tier approach, may determine which one of several multiple fragment molecules has a highest probability of improving binding to a surface region of a large molecule that is proximate to a bound ligand, in order to produce an optimized lead compound for drug discovery.
Description of the Related Art
Fragment-based drug discovery relies on a simple premise: identify small molecule fragments that bind to a target region of a large molecule and then evolve or link the small molecule fragments to create a larger high-affinity molecule. To a first approximation, the binding free-energies of fragments bound in non-overlapping poses are additive (Dill K A (1997) Additivity principles in biochemistry. J Biol Chem 272:701-704). Therefore, linking two such fragments with millimolar affinities, e.g., 4 kcal*mol−1, will yield a single fragment molecule with a micromolar affinity of, e.g., 8 kcal*mol−1, which is of sufficient affinity to serve as a “hit” for lead compound optimization in fragment-based drug discovery (Erlanson D A et al. (2004) Fragment-based drug discovery. J Med Chem 47:3463-3482). Since the 3-dimensional chemical space spanned by small fragments is orders of magnitude smaller than that spanned by molecules of sufficient size to be hits, it is feasible to screen a fragment library representative of the full extent of chemical space (Congreve M et al. (2008) Recent developments in fragment-based drug discovery. J Med Chem 51:3661-3680).
Nature imposes an upper limit on the contribution per ligand heavy atom to the binding free-energy (Kuntz I D et al. (1999) The maximal affinity of ligands. Proc Natl Acad Sci USA 96:9997-10002), commonly referred to as ligand efficiency, (LE) (Hopkins A L et al. (2004) Ligand efficiency: a useful metric for lead selection. Drug Discov Today 9:430-431). This limit means that even the best fragments, having an LE value of 0.4-0.5 kcal*mol−1 per heavy atom, still have weak affinities for their target regions, making their screening by traditional assays difficult. Consequently, fragment-based drug discovery relies on sensitive biophysical methods to detect fragment binding.
Among these biophysical methods are NMR spectroscopy and x-ray crystallography. These two methods additionally yield 3-dimensional structural information about fragment binding poses, which is useful for confirming that two fragments indeed bind to two adjacent sites and can be productively linked. However, despite the utility of NMR spectroscopy and x-ray crystallography to detect fragment binding, there are significant time, labor, and materials costs associated with these two biophysical fragment-based drug discovery approaches.
Computational approaches to fragment-based drug discovery can mitigate the costs of experimental fragment-based drug discovery based on such biophysical techniques as, NMR spectroscopy and x-ray crystallography. Currently, in computational approaches, the large molecule is assumed to be rigid and fragments populate the surface of the, for example, rigid protein molecule using an energy function that models the solvent environment as a continuum (Miranker A, et al. (1991) Functionality maps of binding-sites—a multiple copy simultaneous search method. Proteins: Struct, Funct, Genet 11:29-34; Carlson H A, et al. (2000) Developing a dynamic pharmacophore model for HIV-1 integrase. J Med Chem 43:2100-2114; Landon M R, et al. (2007) Identification of hot spots within druggable binding regions by computational solvent mapping of proteins. J Med Chem 50:1231-1240). As a result, these computational approaches are limited in their ability to accurately account for the exemplary protein molecule's conformational heterogeneity and solvation effects, contributions that are essential to compute free energies of binding of small molecules to the target region of the exemplary protein molecule (Guvench O, et al. (2009) Computational evaluation of protein-small molecule binding. Curr Opin Struct Biol 19:56-61).
In reality, protein molecules can accommodate ligands by undergoing conformational changes (Arkin M R, et al. (2004) Small-molecule inhibitors of protein-protein interactions: Progressing towards the dream. Nat Rev Drug Discovery 3:301-317), and water plays an important role in protein:ligand binding affinity (Lu Y P, et al, (2006) Binding free energy contributions of interfacial waters in HIV-1 protease/inhibitor complexes. J Am Chem Soc 128:11830-11839; Young T, et al. (2007) Motifs for molecular recognition exploiting hydrophobic enclosure in protein-ligand binding Proc Natl Acad Sci USA 104:808-813). Significant recent advances have been made with regard to both incorporating protein flexibility, for example by screening against multiple different rigid protein conformations (Bowman A L, et al. (2007) Small molecule inhibitors of the MDM2-p53 interaction discovered by ensemble-based receptor models. J Am Chem Soc 129:12809-12814; Amaro R E, et al. (2008) An improved relaxed complex scheme for receptor flexibility in computer-aided drug design. J. Comput-Aided. Mol. Des. 22:693-705; Totrov M, et al. (2008) Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr Opin Struct Biol 18:178-184), and more accurate modeling of solvation effects in energy functions (Abel R, Young T, Farid R, Berne B J, Friesner R A (2008) Role of the active-site solvent in the thermodynamics of Factor Xa ligand binding. J Am Chem Soc 130:2817-2831). Nonetheless, approximations used in computational approaches to date still limit the accuracy of fragment placement and fragment scoring, which relates to affinity of the fragment for the binding site, and, ultimately, the determination of the most suitable fragment for a selected binding-site region of the protein molecule.
All-atom explicit-solvent molecular dynamics (MD) simulations of proteins give an atomic-level-of-detail description of the motions of both a large biomolecule, for example, a protein, and water atoms at relevant temperature and pressure (Karplus M, et al. (2002) Molecular dynamics simulations of biomolecules. Nat Struct Biol 9:646-652). In this case, the MD computational simulation samples a Boltzmann distribution of thermally accessible protein conformations, and with the ability of the MD simulation to reach the nanosecond timescale, the sampled conformations include changes in sidechain dihedral angles as well as loop motions of the protein.
Furthermore, MD simulation-based methods are able to rigorously determine the absolute binding free energy of a ligand to a protein molecule (Woo H J, et al. (2005) Calculation of absolute protein-ligand binding free energy from computer simulations. Proc Natl Acad Sci USA 102:6825-6830; Deng Y Q, et al. (2006) Calculation of standard binding free energies: Aromatic molecules in the T4 lysozyme L99A mutant. J Chem Theory Comput 2:1255-1273; Wang J, et al. (2006) Absolute binding free energy calculations using molecular dynamics simulations with restraining potentials. Biophys J 91:2798-2814; Jiao D, et al. (2008) Calculation of protein-ligand binding free energy by using a polarizable potential. Proc Natl Acad Sci USA 105:6290-6295; Lee M S, et al. (2006) Calculation of absolute protein-ligand binding affinity using path and endpoint approaches. Biophys J 90:864-877; Lee M S, et al. (2008) Calculation of absolute ligand binding free energy to a ribosome-targeting protein as a function of solvent model. J Phys Chem B 112:13411-13417). However, such MD simulated free-energy calculations are computationally expensive, limiting MD simulations from being used directly for high-throughput in silico screening.
There remains a need for a method of computational chemistry that identifies the binding sites of small molecules and small molecule fragments to a large molecule while (1) mitigating the costs of NMR spectroscopy and x-ray crystallography of experimental fragment-based drug discovery and (2) including detailed representations of both a large molecule's conformational heterogeneity and solvation effects.